We use long-run annual cross-country data for 10 macroeconomic variables to evaluate the long-horizon forecast distributions of six forecasting models. The variables we use range from ones having little serial correlation to ones having persistence consistent with unit roots. Our forecasting models include simple time series models and frequency domain models developed in Müller and Watson (2016). For plausibly stationary variables, an AR(1) model and a frequency domain model that does not require the user to take a stand on the order of integration appear reasonably well calibrated for forecast horizons of 10 and 25 years. For plausibly non-stationary variables, a random walk model appears reasonably well calibrated for forecast horizons of 10 and 25 years.
That is from a new NBER working paper by Kurt G. Lunsford and Kenneth D. West. If you do not know macro, here is a GPT translation in plainspeak. And this new paper suggests macro shocks do not matter that much.
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